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Predictive Source Control: Application of Advanced Numerical Methods and Surrogate Monitoring for Microconstituents in Water Reclamation

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Having the water reclamation capacity of 70 million gallons per day (MGD) and expandable up to 130 MGD, the Ground Water Replenishment (GWR) System is the largest water purification and reuse project of its kind in the world. With the advent of groundwater replenishment and reuse, a new paradigm of source control has started at the Orange County Sanitation District (OCSD). Traditionally, Source Control was primarily based on enforcement and compliance of the EPA's National Pretreatment and NPDES Programs. At OCSD, Source Control has expanded its role to monitoring and controlling microconstituents from both point and nonpoint sources using two innovative methods: 1) Predictive Modeling and 2) Real-Time Modeling.

As part of its expanded program to assure that the water produced by the GWR System is of the highest quality, Source Control has developed key programs for both predictive and real-time monitoring of microconstituents in source water received by the facilities. Information from predictive and real-time monitoring can be used to alert facility operations of abnormalities such as microconstituent concentration spikes, growth trends, or reductions. The information is also used as triggers for implementation of various point and nonpoint source control measures to reduce or smooth concentration spikes.

Predictive Modeling

Predictive modeling is based on a stochastic, time-series approach using Kalman filters and autoregressive integrated moving average methods such as Box-Jenkins. These models are applied to microconstituent analytical data of 1) the source stream recognizing that there is a high potential of outliers and noise due to matrix interference and signal suppression; and 2) intermediate streams where noise due to matrix interference and signal suppression are diminished but facility dynamics come into play.

Unlike conventional constituents where levels of concentrations are established or exhibit discernable patterns, microconstituents may follow consumer trends that may be influenced by competing new products, product replacement and substitution, health advisories, or consumer news. Historical (past) data can potentially bias the results if it is not discarded after new consumer trends emerge. Therefore, a recursive approach is necessary where the output is used to condition the estimate and past data is discarded.

Results of the stochastic time series model are compared with economic indicators such as pharmaceutical sales estimates or usage statistics, and heuristics based on expert opinion and product literature. In this manner, the predictive model is tempered with real-life judgment.

Real-Time Modeling

Real-time modeling uses surrogates and analytical equipment to identify the magnitude of concentration and upstream locations of point and nonpoint sources or in-plant. Statistical methods are employed that test for correlation of the surrogate to speciated analytical data. If the surrogate concentration increases, it may indicate an increase in concentration of a family of microconstituents. The use of surrogates, in effect, reduces the time and cost of tracking individual microconstituents. Control and action levels for surrogates are set by a mass balance around the facilities using regulatory limits, standards, or guidelines and removal efficiencies derived analytical values of microconstituents and stream flow rates.

Predictive and real-time models for microconstituents will be developed based on analytical results and flows from the GWR System and Reclamation Plant No. 1. The results will be discussed relative to practical (day-to-day) implementation and use.

Keywords: Dynamics; Groundwater Replenishment; Kalman Filer; Microconstituents; Predictive Modeling; Real-Time Modeling; Robust Regression on Order; Surrogates

Document Type: Research Article


Publication date: January 1, 2009

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